Data teams are crucial to the success of any data-driven organisation, and their structure can significantly impact their effectiveness. According to our research, data teams can be broadly structured in three ways: centralised, decentralised, or hybrid.
In a centralised structure, data teams are organised as a single unit within a company, and their decision-making powers and reporting structure are clear. On the other hand, in a decentralised structure, data teams are embedded within specific business units, providing tailored insights according to the needs of the unit. Finally, hybrid (or matrix) structures are a mix of both, allowing for greater collaboration and innovation.
This report, by AIM Research, seeks to understand how data science teams across several organisations are structured, based on factors such as size, age, and headquarters. It discusses the pros and cons of each structure and the value it adds to the organisation, as well as identifies the distribution of companies that have adopted each working model.
AIM Daily XO
Join our editors every weekday evening as they steer you through the most significant news of the day, introduce you to fresh perspectives, and provide unexpected moments of joy
To help readers better understand how these structures work in practice, the report includes several case studies. These case studies illustrate how companies across different industries have implemented various data team structures and the benefits they have seen as a result.
In conclusion, choosing the right data team structure depends on the specific needs of the organisation. By understanding the pros and cons of each structure and examining real-world examples, organisations can make informed decisions that lead to successful data-driven initiatives.